Official agent skill

Querying AWS Sagemaker Catalog

Runs SQL analytics on SageMaker Catalog asset metadata tables exported as Apache Iceberg in S3 Tables.

OfficialApache-2.0Auto-check passedData & Analytics

Install Querying AWS Sagemaker Catalog

skills CLI

$ npx skills add aws/agent-toolkit-for-aws --skill querying-aws-sagemaker-catalog -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install aws/agent-toolkit-for-aws querying-aws-sagemaker-catalog --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog .claude/skills/querying-aws-sagemaker-catalog && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "querying-aws-sagemaker-catalog" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog into .claude/skills/querying-aws-sagemaker-catalog/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "querying-aws-sagemaker-catalog", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add aws/agent-toolkit-for-aws --skill querying-aws-sagemaker-catalog -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install aws/agent-toolkit-for-aws querying-aws-sagemaker-catalog --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog .agents/skills/querying-aws-sagemaker-catalog && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "querying-aws-sagemaker-catalog" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog into .agents/skills/querying-aws-sagemaker-catalog/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "querying-aws-sagemaker-catalog", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add aws/agent-toolkit-for-aws --skill querying-aws-sagemaker-catalog -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install aws/agent-toolkit-for-aws querying-aws-sagemaker-catalog --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog .cursor/skills/querying-aws-sagemaker-catalog && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "querying-aws-sagemaker-catalog" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog into .cursor/skills/querying-aws-sagemaker-catalog/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "querying-aws-sagemaker-catalog", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/aws/agent-toolkit-for-aws.git --path skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add aws/agent-toolkit-for-aws --skill querying-aws-sagemaker-catalog -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install aws/agent-toolkit-for-aws querying-aws-sagemaker-catalog --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog .gemini/skills/querying-aws-sagemaker-catalog && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "querying-aws-sagemaker-catalog" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog into .gemini/skills/querying-aws-sagemaker-catalog/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "querying-aws-sagemaker-catalog", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install aws/agent-toolkit-for-aws querying-aws-sagemaker-catalog

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add aws/agent-toolkit-for-aws --skill querying-aws-sagemaker-catalog -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog .github/skills/querying-aws-sagemaker-catalog && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "querying-aws-sagemaker-catalog" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog into .github/skills/querying-aws-sagemaker-catalog/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "querying-aws-sagemaker-catalog", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add aws/agent-toolkit-for-aws --skill querying-aws-sagemaker-catalog -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install aws/agent-toolkit-for-aws querying-aws-sagemaker-catalog --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog .opencode/skills/querying-aws-sagemaker-catalog && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "querying-aws-sagemaker-catalog" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog into .opencode/skills/querying-aws-sagemaker-catalog/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "querying-aws-sagemaker-catalog", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: querying-aws-sagemaker-catalog
GitHub stars: 2.8k
Token cost: ~2.6k tokens
SKILL.md length: 774 words
Files: 1
Skills in repo: 138
Repo updated: Oct 9, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Runs SQL analytics on SageMaker Catalog asset metadata tables exported as Apache Iceberg in S3 Tables.

Works in 4 steps: Check If Configured → Enable → Verify Permissions for Querying → …
Phrases: catalog inventory SQL
SKILL.md covers Overview, Decision Tree, Common Tasks and Key Behaviors, plus 3 more sections
Calls aws

What it does

Querying AWS Sagemaker Catalog is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Runs SQL analytics on SageMaker Catalog asset metadata tables exported as Apache Iceberg in S3 Tables. Covers governance queries, asset growth tracking, ownership audits, time-travel over catalog state, and metadata quality analysis. Applies when querying catalog inventory, finding assets without descriptions, comparing catalog snapshots, or auditing data ownership. Trigger phrases: catalog inventory SQL, how many assets, assets without descriptions, asset growth over time, who owns this data, catalog governance…

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Data governance, File uploads and storage and SQL. It works with Amazon SageMaker, Amazon Web Services and SQL. The repository describes itself as: Official, AWS-supported MCP servers, skills, and plugins to help AI agents build on AWS. The licence is Apache-2.0.

When your agent uses it

Phrases: catalog inventory SQL
How many assets
Assets without descriptions
Asset growth over time

Example prompts

“/querying-aws-sagemaker-catalog”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

Check If Configured
Enable
Verify Permissions for Querying
Query

What it can do on your machine

Read from SKILL.md and the folder at commit df2ab44. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- aws
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- docs.aws.amazon.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Querying AWS Sagemaker Catalog loads about 2.6k tokens when it runs. Until then it costs about 147 tokens; SKILL.md has 774 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~147
When it runs · the whole SKILL.md, loaded when a task matches: ~2.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aws/agent-toolkit-for-aws at commit df2ab44, republished under its Apache-2.0 licence (© aws). 774 words, ~2,630 tokens.

Download SKILL.mdSave it as .claude/skills/querying-aws-sagemaker-catalog/SKILL.md (or your agent's skills folder).

name: querying-aws-sagemaker-catalog
description: Runs SQL analytics on SageMaker Catalog asset metadata tables exported as Apache Iceberg in S3 Tables. Covers governance queries, asset growth tracking, ownership audits, time-travel over catalog state, and metadata quality analysis. Applies when querying catalog inventory, finding assets without descriptions, comparing catalog snapshots, or auditing data ownership. Trigger phrases: catalog inventory SQL, how many assets, assets without descriptions, asset growth over time, who owns this data, catalog governance, data quality audit, catalog analytics.
version: 1
argument-hint: [query|domain-id|'configure'|'status']

Query AWS SageMaker Catalog System Tables

Overview

Works best with the AWS MCP server for sandboxed execution and audit logging. All commands below use the AWS CLI and work in any environment with configured AWS credentials.

Amazon SageMaker Unified Studio (whose catalog feature is referred to below as SageMaker Catalog) exports asset metadata as a daily-snapshot Apache Iceberg table in the AWS-managed aws-sagemaker-catalog table bucket. This enables SQL queries over your entire data catalog inventory — asset counts, governance gaps, ownership audits, and historical comparisons — without building custom ETL.

Data is partitioned by snapshot_time and exported once daily (around midnight per region). The table is read-only.

Decision Tree

User intent	Use this skill?	Alternative
SQL analytics on catalog state (counts, governance, trends)	Yes	—
Historical comparison ("what changed in catalog last week")	Yes — time travel via `snapshot_time`	—
Find assets without owners or descriptions	Yes	—
Find a specific table by name or concept	No	`finding-data-lake-assets` or Glue Discovery `search`
Browse/enumerate catalog interactively	No	`exploring-data-catalog`
Run a query on a table's data	No	`querying-data-lake`
Manage catalog metadata (add descriptions, tags)	No	Glue Discovery `put-form-type` / `associate-glossary-terms`

Common Tasks

1. Check If Configured

bash

aws datazone get-data-export-configuration \
  --domain-identifier <DOMAIN_ID> \
  --region <REGION>

If no domain exists: aws datazone list-domains --region <REGION>
If export not enabled: guide user to enable.
One domain per account per region.

Verify table bucket exists:

bash

aws s3tables list-table-buckets --region <REGION> \
  --query "tableBuckets[?name=='aws-sagemaker-catalog']"

2. Enable

With KMS encryption (recommended for production):

bash

aws datazone put-data-export-configuration \
  --domain-identifier <DOMAIN_ID> \
  --region <REGION> \
  --enable-export \
  --encryption-configuration kmsKeyArn=<KMS_KEY_ARN>,sseAlgorithm=aws:kms

Note: Encryption cannot be changed after creation. Always specify KMS for sensitive catalog data.

Without encryption (for quick testing only):

bash

aws datazone put-data-export-configuration \
  --domain-identifier <DOMAIN_ID> \
  --region <REGION> \
  --enable-export

First data available within 24 hours. See: Exporting asset metadata

3. Verify Permissions for Querying

Requires:

S3 Tables federated catalog registered in Glue (s3tablescatalog)
Lake Formation SELECT + DESCRIBE grants on the table

Grant access:

bash

aws lakeformation grant-permissions \
  --principal DataLakePrincipalIdentifier=<ROLE_ARN> \
  --resource '{"Table": {"CatalogId": "<ACCOUNT>:s3tablescatalog/aws-sagemaker-catalog", "DatabaseName": "asset_metadata", "Name": "asset"}}' \
  --permissions DESCRIBE SELECT \
  --region <REGION>

4. Query

Query syntax:

sql

"s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset"

Constraints:

You MUST always filter by snapshot_time — without it, the query scans all historical snapshots and returns duplicates
You MUST confirm workgroup and output location before executing
Default to DATE(snapshot_time) = CURRENT_DATE for current state

You SHOULD use the key columns documented in this skill to build queries. If you need the full schema, run get-tables once:

aws glue get-tables --catalog-id "<ACCOUNT>:s3tablescatalog/aws-sagemaker-catalog" --database-name "asset_metadata" --region <REGION>

Key columns:

Column	What it holds	Usage
`snapshot_time`	Partition key — daily snapshot timestamp	Always filter on this
`asset_id`	Unique catalog asset identifier	Primary key for lookups
`resource_type_enum`	GlueTable, RedshiftTable, S3Collection, etc.	Filter by asset type
`resource_id`	ARN or native identifier	Cross-reference with source systems
`asset_name`	Business-friendly name	Display, search
`resource_name`	Technical name (table name, prefix)	Filtering
`business_description`	Business context (NULL if not provided)	Governance gaps
`extended_metadata`	`map<string,string>` — flexible key-value attributes	Use bracket notation: `extended_metadata['owningEntityId']`
`asset_created_time`	When asset first appeared in catalog	Growth analysis
`asset_updated_time`	Last modification time	Freshness checks

Current catalog state:

sql

SELECT resource_type_enum, COUNT(*) as count
FROM "s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset"
WHERE DATE(snapshot_time) = CURRENT_DATE
GROUP BY resource_type_enum
ORDER BY count DESC;

Assets without business descriptions:

sql

SELECT asset_name, resource_name, resource_type_enum, account_id
FROM "s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset"
WHERE DATE(snapshot_time) = CURRENT_DATE
  AND business_description IS NULL;

Asset growth over last 30 days:

sql

SELECT DATE(snapshot_time) as date, COUNT(*) as total_assets
FROM "s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset"
WHERE DATE(snapshot_time) >= CURRENT_DATE - INTERVAL '30' DAY
GROUP BY DATE(snapshot_time)
ORDER BY date DESC;

Time travel — compare current vs 7 days ago (new descriptions added):

sql

SELECT t.asset_id, t.resource_name,
       p.business_description as before,
       t.business_description as now
FROM "s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset" t
JOIN "s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset" p
  ON t.asset_id = p.asset_id
WHERE DATE(t.snapshot_time) = CURRENT_DATE
  AND DATE(p.snapshot_time) = CURRENT_DATE - INTERVAL '7' DAY
  AND p.business_description IS NULL
  AND t.business_description IS NOT NULL;

Assets by owner:

sql

SELECT extended_metadata['owningEntityId'] as owner, COUNT(*) as count
FROM "s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset"
WHERE DATE(snapshot_time) = CURRENT_DATE
  AND extended_metadata['owningEntityId'] IS NOT NULL
GROUP BY extended_metadata['owningEntityId']
ORDER BY count DESC;

Filter by metadata form field:

sql

SELECT *
FROM "s3tablescatalog/aws-sagemaker-catalog"."asset_metadata"."asset"
WHERE DATE(snapshot_time) = CURRENT_DATE
  AND extended_metadata['<metadata-form-name>.<field-name>'] = '<field-value>';

Show full SKILL.md (317 more words)Show less

Key Behaviors

Daily snapshots — exported around midnight per region
Always filter by snapshot_time — without it you get all history (duplicates, slow)
One domain per account per region — to switch domains, delete config first
No additional charge beyond S3 Tables storage + Athena queries
Read-only — to update asset metadata, use Glue Discovery APIs or SageMaker Unified Studio

Troubleshooting

Error	Cause	Fix
`aws-sagemaker-catalog` bucket not found	Export not enabled	Run `put-data-export-configuration --enable-export`
Empty results with `CURRENT_DATE`	First export hasn't run yet (takes up to 24h)	Wait; try yesterday's date
`AccessDenied` on query	Missing Lake Formation grants	Grant SELECT + DESCRIBE on the table
`CATALOG_NOT_FOUND`	S3 Tables not registered in Glue	Enable integration: S3 console > Table buckets > Enable integration
Duplicate rows in results	Missing `snapshot_time` filter	Add `WHERE DATE(snapshot_time) = CURRENT_DATE`
`extended_metadata` key returns NULL	Key doesn't exist for that asset	Check available keys: `SELECT DISTINCT key FROM ... CROSS JOIN UNNEST(map_keys(extended_metadata)) AS t(key) WHERE DATE(snapshot_time) = CURRENT_DATE`
Cannot update export encryption	Encryption set at creation time only	Delete and recreate export config

Security Considerations

Data sensitivity: Catalog metadata exposes organizational structure including asset names, ownership, account IDs, naming conventions, and internal resource identifiers. Treat query results as sensitive by default.

Encryption at rest: Always enable KMS encryption when creating the export configuration. Encryption cannot be changed after creation. Additionally, configure SSE-KMS on your Athena workgroup output bucket.

Least-privilege access: Grant Lake Formation SELECT + DESCRIBE only on the specific asset_metadata.asset table to roles that need catalog analytics. Avoid granting access to the entire aws-sagemaker-catalog bucket.

Audit trail: Enable CloudTrail logging for DataZone (PutDataExportConfiguration, GetDataExportConfiguration), Athena (StartQueryExecution, GetQueryResults), and S3 Tables API calls to track who queries catalog metadata.

Credential hygiene: Use IAM roles with temporary credentials for querying. Avoid long-lived access keys for users accessing catalog metadata. Scope down or rotate principals when access is no longer needed.

Additional Resources

Files

Just SKILL.md in skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog of aws/agent-toolkit-for-aws.

Open the folder on GitHubat commit df2ab44

Compare with similar skills

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Works with

Questions about Querying AWS Sagemaker Catalog

What does Querying AWS Sagemaker Catalog do?

Runs SQL analytics on SageMaker Catalog asset metadata tables exported as Apache Iceberg in S3 Tables. Querying AWS Sagemaker Catalog is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Runs SQL analytics on SageMaker Catalog asset metadata tables exported as Apache Iceberg in S3 Tables.

When should I use Querying AWS Sagemaker Catalog?

Querying AWS Sagemaker Catalog fits situations like: phrases: catalog inventory SQL; how many assets; assets without descriptions; asset growth over time.

How do I install Querying AWS Sagemaker Catalog in Claude Code?

Run `npx skills add aws/agent-toolkit-for-aws --skill querying-aws-sagemaker-catalog -a claude-code`. Or copy the skill folder (skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog in aws/agent-toolkit-for-aws) into .claude/skills/querying-aws-sagemaker-catalog in your project. Claude Code loads it when a task matches its description.

How do I install Querying AWS Sagemaker Catalog in Codex?

Run `npx skills add aws/agent-toolkit-for-aws --skill querying-aws-sagemaker-catalog -a codex`. Or copy the skill folder (skills/specialized-skills/system-table-skills/querying-aws-sagemaker-catalog in aws/agent-toolkit-for-aws) into .agents/skills/querying-aws-sagemaker-catalog in your project. Codex loads it when a task matches its description.

Can I use Querying AWS Sagemaker Catalog in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aws/agent-toolkit-for-aws --skill querying-aws-sagemaker-catalog -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/querying-aws-sagemaker-catalog, .gemini/skills/querying-aws-sagemaker-catalog, .github/skills/querying-aws-sagemaker-catalog and .opencode/skills/querying-aws-sagemaker-catalog in your project.

What does Querying AWS Sagemaker Catalog need to run?

Going by SKILL.md and its folder, Querying AWS Sagemaker Catalog needs the command-line tools its instructions call (aws).

Does Querying AWS Sagemaker Catalog access the network?

SKILL.md names 1 domain. As links in the text: docs.aws.amazon.com. This is read from the text; nothing was executed.

Is Querying AWS Sagemaker Catalog safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Querying AWS Sagemaker Catalog use?

Querying AWS Sagemaker Catalog is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Querying AWS Sagemaker Catalog use?

About 2.6k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Querying AWS Sagemaker Catalog?

Skills that share tags, products or a category with Querying AWS Sagemaker Catalog: Glue Diagnostics (Kilo-Org/kilo-marketplace, 190 stars), Querying Big Datasets (flyrank-bih/flyrank-ml-internship-starter, 140 stars), Performing Cloud Forensics With AWS Cloudtrail (mukul975/Anthropic-Cybersecurity-Skills, 34k stars) and Performing Cloud Log Forensics With Athena (mukul975/Anthropic-Cybersecurity-Skills, 34k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Querying AWS Sagemaker Catalog?

aws (a GitHub organization, an official publisher) maintains it in aws/agent-toolkit-for-aws, which has 2,830 GitHub stars. The repository holds 138 skills in this directory. The repository was last updated on October 9, 2026.

Source: aws/agent-toolkit-for-aws on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Querying AWS Sagemaker Catalog

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Query AWS SageMaker Catalog System Tables

Overview

Decision Tree

Common Tasks

1. Check If Configured

2. Enable

3. Verify Permissions for Querying

4. Query

Key Behaviors

Troubleshooting

Security Considerations

Additional Resources

Files

Compare with similar skills

Similar skills

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Questions about Querying AWS Sagemaker Catalog